1. Field of the Invention
The invention in general relates to user interfaces for electronic devices, and more particularly to user interfaces for reviewing summarizations of tabular data on electronic devices with smaller displays.
2. Description of the Related Art
A common example of tabular data is a spreadsheet having a plurality of columns, each with a column title indicative of a different data dimension that respectively can have multiple values. Selections of the values are provided in rows, and often a respective numerical quantity is associated with the value selections of each row. An example excerpt from a spreadsheet is illustrated in FIG. 1. Because tabular data can have a number of different dimensions associated with many individual entries, it can be difficult to extract meaning and higher-level conclusions from the data. For example, in a spreadsheet that organizes sales by geographic region and by time-related dimensions (example in FIG. 1), there often will be a separate row entry for each item of sales data for each combination of geographic region and time span. So without a way to produce summaries of the data for higher-level geographic regions and time spans, it may be difficult to make higher-level conclusions from such data.
Pivot tables and data cubes provide mechanisms that can help with aggregating large data sets into summarizations that are more easily digestable, and hence useful for analysis. One view of a data cubes (sometimes also called an OLAP cube) is that a data cube is an extension to the two-dimensional array of a spreadsheet. An example usage for a data cube is that a financial analyst might want to view different summarizations of a data set. FIG. 1 is further described to provide a more specific example.
FIG. 1 shows a screen capture 101 of a portion of a spreadsheet of sales data 115 (column H as depicted) and associated with each row entry of sales data 115 are a plurality of dimensions collectively identified as 106, and which include Region, Territory, Business Type, Year 116, Quarter 117, and Month 118 (Year 116, Quarter 117, and Month 118 are separately numbered and provide an example of a natural hierarchical arrangement of dimensions). Each of these dimensions 105 has a plurality of values, and a selection of a value for each dimension is associated with a particular entry in sales data 115. For example, row 2 includes the value “Americas” for the region dimension and “2006” for the Year dimension associated with the Sales value 88,424.
Since this form of tabular data does not provide any sort of summarization of the sales data 115 according to any of the associated dimensions, mechanisms exist to help with that data summarization, one example of such mechanisms is a pivot table, and another related tool is a data cube. A pivot table is a data summarization tool found in data visualization programs such as spreadsheets. Among other functions, it can automatically sort, count, and total the data stored in one table or spreadsheet and create a second table displaying the summarized data. Pivot tables are useful to create crosstabs quickly. The user sets up and changes the summary's structure by dragging-and-dropping fields graphically. This “rotation” or pivoting of the summary table gives the concept its name.
FIG. 2A shows a screen shot 201 of a portion of a pivot table that can be created from the spreadsheet of FIG. 1. A Sum of Sales 205 is what has been selected to be summarized in the entries of the data columns, starting with column E. Dimensions Region (110), Territory, Business Type, and Product were selected to be dimensions that will cause row-based organization of the pivot table, while the naturally hierarchical dimensions Year, Month, and Quarter were selected as dimensions that will cause column-based organization of the pivot table. Values Americas 105a and Asia 105b are separately identified values within Region dimension 110. Each dimension selected for row-based organization appears in a separate column across a top portion of the pivot table, and are collectively identified as 210.
Since the sum of sales 205 was selected, the values presented at the intersections between the rows and columns present a sum of sales for the values of the dimensions corresponding to those respective rows and columns. For example, column E, row 5 depicts a sum of sales for 2006 for the Corporate business type, within the Central Territory, within the Americas Region.
Each dimension includes a drop down arrow that when selected results in appearance of a picklist, as shown in FIG. 2B for the example dimension Region. By selecting or unselecting values in this picklist, data for different values of the Region dimension can be shown or not shown. At least one value for each dimension must be selected, or the main pivot table organization must be revisited to remove that dimension (e.g., at least one region must be selected). As will be shown below, more granularity or less granularity of data specificity can be obtained by double clicking on different values for the row-based and column-based dimensions. Such double-clicking results in certain behaviors, some of which are summarized below.
Both the spreadsheet and the pivot table of these examples are very modest in size, and actual pivot tables can be substantially bigger in terms of the amount of data, including a number of dimensions, and a number of values appearing in each dimension (e.g., a SKU dimension could have hundreds of entries, a large retail chain could have hundreds of stores and thousands of SKUs). Thus, a pivot table can be extremely large.
Users have managed to use such pivot tables to advantage, and have benefitted from relatively large and still growing screen sizes available on desktop computers. However, mobile devices are proliferating and growing in an amount of available computer power. It would be desirable to be able to access such pivot tables from mobile devices, including laptops, smart phones and the like. These devices generally have smaller screens, and usually, more portable devices, such as smart phones have even smaller screens that portable devices such as laptops.
FIG. 2A illustrates an example display 220 of a mobile device, such that anything within the interior of display 220 would be what is viewed by a user of the mobile device. Thus, FIG. 2A further illustrates that a user of a mobile device having display 220 would be able to view only a very limited portion of even the small pivot table here.
FIG. 3 illustrates display 220 superimposed on another view of the same pivot table. Comparing FIG. 2A to FIG. 3, the difference between them illustrates that a user can double click on the “Central” value of the Territory dimension for the Americas region (i.e., Row 5, Column B contains the value “Central” for the Territory dimension, and this row also corresponds to the value “Americas” of the Region dimension. The pivot table responds by causing all the values for the Business Type and Product dimensions to be hidden, for all of the values of the Region dimension (i.e., including for values “Asia” and “Europe”). However, even with all of that extra detail hidden, not much of the pivot table can be viewed at one time, and it is apparent, for example, that data values even for the first year 2006 would not be visible at the same time as the Territory values to which those data values correspond.
FIG. 4 illustrates a further view 401 of the same pivot table, also having display 220 superimposed thereon. In view 401, a more expanded view of the row dimensions (See FIG. 2A) are depicted. This view 401 can be arrived at from the pivot table view of FIG. 3 first by double clicking on any of the Central values for any of the Regions (e.g., either the Central value for the Americas or Asia Region can be clicked, with the same result), then any of the then displayed Corporate values of Business type are double clicked, resulting in display of the Product values shown in view 401 (identified as 411, 312, and 413). At each step, a summarization of sales would be shown in the field data appropriate for that view. In other words, after one double click on Central, then corporate and retail values would be shown, and sales summarization for 2006 would be shown in column E.
From the view of FIG. 4, the view of FIG. 3 can be obtained again by double clicking only on any of the Central values of any Territory (i.e., the entire expanded view collapses with one double click.) However, the pivot table, if any of the Central regions is again clicked, would return to the view of FIG. 4, and would not first display only sum of sales data for Business type, for example. In other word, the pivot table remembers a previous degree of expansion and returns to that degree, rather than going through intermediate values. To cause the pivot table to traverse the intermediate dimensions requires double clicking on each intermediate dimension.
FIG. 5 illustrates an example of the same pivot table, where the Corporate value 505 in Row 5, Column C was selected, causing all the Product dimension values to be hidden. If the Central value of Column B, Row 5 were then double clicked, the view would return to that of FIG. 3, and if that Central value were double clicked again, the view would return to that of FIG. 5, not that of FIG. 4.
FIG. 6 also illustrates a screen capture 601 of a portion of the same pivot table. FIG. 6 is for illustrating another behavior of a pivot table. FIG. 6 illustrates that the column dimensions are expanded, such that the Year 2006 dimension also has displayed Q1, Q2, and Q3. Q2 has displayed months April, May, and June. The months April, May, and June were shown after double clicking on the Q2 value 605. The data values appearing in columns to the right of Q2 were moved farther to the right after displaying the constituent values under Q2. In other words, all that data still appears on the same page, and horizontally or vertically scrolling could cause portions of that data to be displayed within a display window.
FIG. 7 illustrates another screen capture 701 of a portion of the same pivot table, with two different instantiations of an example small display 705, and 706. Screen capture 701 is for illustrating that an option to freeze a view of both row and column dimensions can be selected, which in this case causes columns A-G to be frozen (collectively identified as 710), and as can be discerned by observing that after column G, the next column displayed is column AD. Unfrozen columns displayed are identified as 711. However, even with freezing these columns, on a small display, little context for the data that is currently displayed on the display is evident, as shown by the examples 705 and 706. For example, it would be difficult to discern what territory the value at Column G, row 7 was for. In a larger pivot table, all of the above behaviors would become even more problematic for viewing such information on a small display, such as that of a smart phone.